I think you can adapt the code of the notebook to sample from the channel contributions as
y_out_of_sample = mmm.sample_posterior_predictive(
X_pred=X_out_of_sample,
extend_idata=False,
var_names=["y", "channel_contributions"],
)
Let me know if this works for you ![]()